Video deblurring based on bidirectional motion compensation and accurate blur kernel estimation

Lee, Dong-bok; Heo, Bo-Young; Song, Byung Cheol

doi:10.1109/icip.2013.6738185

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…In the asymmetric resolution framework, the left and the right reference frames I L,LR1 and I R are the synchronized frames of the left and right views. In the algorithm for fixed support window, normalized mutual information (NMI) C NMI is treated as the matching measurement for the two symmetrical support windows and is expressed as (1) N p d (x, y) is the joint probability, and…”

Section: Synchronized Frames Enhancement By Disparity Estimationmentioning

confidence: 99%

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A resolution enhancement algorithm for an asymmetric resolution stereo video

Liu

Zhao

et al. 2015

J Image Video Proc.

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This paper presents a resolution enhancement algorithm in an asymmetric resolution stereo video for improving the video resolution. In this stereo video architecture, a scene is captured by two cameras to form two views, one view is a lower-resolution video and the other is a full-resolution video. The goal is to enhance the lower-resolution video to a full-resolution video. In the lower-resolution video, frames synchronized with full-resolution video are enhanced via disparity estimation algorithm, while the rest frames are improved by mono-view video super-resolution based on key frames method. The experimental results demonstrate that the proposed method is effective for both visual and objective qualities.

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Section: Synchronized Frames Enhancement By Disparity Estimationmentioning

confidence: 99%

“…Video super-resolution algorithms are ubiquitously used for mono-view video enhancement [1][2][3][4][5][6][7][8]. Among the video super-resolution algorithms, one strategy is to reconstruct the HR frames in a mixed resolution framework (a mono-view mixed resolution video has both HR frames and LR frames).…”

Section: Introductionmentioning

confidence: 99%

A resolution enhancement algorithm for an asymmetric resolution stereo video

Liu

Zhao

et al. 2015

J Image Video Proc.

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“…Other methods take into account the temporal coherence between video frames in the blur kernel estimation or the latent sharp frame restoration [ 29 , 30 , 31 , 32 , 33 ]. Lee et al [ 29 , 30 ] utilized the high-resolution information of adjacent unblurred frames to reconstruct blurry frames. This method can accelerate the precise estimation of the blur kernel, but meanwhile it assumes that the video is sparsely blurred.…”

Section: Introductionmentioning

confidence: 99%

Combining Motion Compensation with Spatiotemporal Constraint for Video Deblurring

Gong

2018

Sensors

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We propose a video deblurring method by combining motion compensation with spatiotemporal constraint for restoring blurry video caused by camera shake. The proposed method makes effective full use of the spatiotemporal information not only in the blur kernel estimation, but also in the latent sharp frame restoration. Firstly, we estimate a motion vector between the current and the previous blurred frames, and introduce the estimated motion vector for deriving the motion-compensated frame with the previous restored frame. Secondly, we proposed a blur kernel estimation strategy by applying the derived motion-compensated frame to an improved regularization model for improving the quality of the estimated blur kernel and reducing the processing time. Thirdly, we propose a spatiotemporal constraint algorithm that can not only enhance temporal consistency, but also suppress noise and ringing artifacts of the deblurred video through introducing a temporal regularization term. Finally, we extend Fast Total Variation de-convolution (FTVd) for solving the minimization problem of the proposed spatiotemporal constraint energy function. Extensive experiments demonstrate that the proposed method achieve the state-of-the-art results either in subjective vision or objective evaluation.

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Combining weighted curvelet accumulation with motion vector duty cycle for nonuniform video deblurring

Gong

Zhan

et al. 2019

Signal Processing: Image Communication

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Video deblurring based on bidirectional motion compensation and accurate blur kernel estimation

Cited by 3 publications

References 17 publications

A resolution enhancement algorithm for an asymmetric resolution stereo video

A resolution enhancement algorithm for an asymmetric resolution stereo video

Combining Motion Compensation with Spatiotemporal Constraint for Video Deblurring

Combining weighted curvelet accumulation with motion vector duty cycle for nonuniform video deblurring

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